ArticleJournal of medical Internet research2015
The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain.
Article in Journal of medical Internet research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- Article
- Has COVID-19 changed carer's views of health and care integration in care homes? A sentiment difference-in-difference analysis of on-line service reviews.Health policy (Amsterdam, Netherlands) · 2022Article
- Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches.Frontiers in pain research (Lausanne, Switzerland) · 2022Review
- Sentiment Analysis in Health and Well-Being: Systematic Review.JMIR medical informatics · 2020Review
- Social Media Content About Children's Pain and Sleep: Content and Network Analysis.JMIR pediatrics and parenting · 2018Article
- Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study.Journal of medical Internet research · 2018Article
- Sentiment Analysis of Health Care Tweets: Review of the Methods Used.JMIR public health and surveillance · 2018Review
- Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California.Journal of medical Internet research · 2017Article
- How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets.JMIR public health and surveillance · 2017Article
- Using Social Media to Characterize Public Sentiment Toward Medical Interventions Commonly Used for Cancer Screening: An Observational Study.Journal of medical Internet research · 2017Observational
- Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter.JMIR public health and surveillance · 2017Article
- Temporal Trends of Psychosociobehavioral Factors Underlying Tobacco Use: A Semi-Automated Exploratory Analysis of Peer-to-Peer Communication in a Health-Related Online Community.Studies in health technology and informatics · 2017Article
- Utilizing Big Data and Twitter to Discover Emergent Online Communities of Cannabis Users.Substance abuse : research and treatment · 2017Article
- Tweeting back: predicting new cases of back pain with mass social media data.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Pediatric injury information seeking for mothers with young children: The role of health literacy and ehealth literacy.Journal of communication in healthcare · 2016Article
- Using Twitter Data to Gain Insights into E-cigarette Marketing and Locations of Use: An Infoveillance Study.Journal of medical Internet research · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundDespite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media.
objectiveThe aim was to examine the type, context, and dissemination of pain-related tweets.
methodsWe used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks.
resultsThe most common terms published in conjunction with the term "pain" included feel (n=1504), don't (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13% in Manila to 56% in Los Angeles, CA, with a median of 29% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24% at 1600 to 38% at 2400, with a median of 32%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25).
conclusionsTaken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters' perceptions of pain and how such perceptions may affect therapies for pain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.